### Machine Learning 101: Key Terminologies Explained

Discover key machine learning terminologies: algorithms, datasets, neural networks, and more in this easy-to-understand guide to AI basics.

Discover key machine learning terminologies: algorithms, datasets, neural networks, and more in this easy-to-understand guide to AI basics.

Explore basic machine learning concepts in this guide. Learn about types of machine learning, key terms, and real-world applications.

Learn how to visualize data and find an optimal regression model using lazypredict package with alltime top 500 movies dataset.

Learn from this simple example about how to make a machine learning model for classifying 3D figures, in this case, from 3D MNIST dataset.

Learn how to make a simple speech recognition algorithm using Google's machine learning library Tensorflow.

Explore activation functions in neural networks, their types, and how to choose the right one for optimal deep learning performance.

Explore random forest regression, including fundamentals, advantages, limitations, applications, and performance improvement techniques.

Discover artificial neural networks for regression, network architecture, optimization, and performance-enhancing techniques.

Explore stochastic gradient descent, its advantages, limitations, applications, and hyperparameter tuning.

Explore polynomial regression with this guide, covering its foundations, advantages, limitations, and practical applications.

Explore elastic net in linear regression, combining lasso and ridge strengths for improved model performance.

Improving linear regression with lasso regression models to reduce overfitting and perform feature selection.